Multiple-electrode neuronal recording has created tremendous new opportunities for gaining insight into the neural substrate of behavior, and it has made possible the construction of brain-controlled robotic devices. A crucial problem is to describe evolution of neuronal activity during learning, which is of interest not only from the point of view of basic science, but also because knowledge of the changes that occur while a subject learns a task is necessary for the construction of reliable neural prosthetic algorithms. This research will develop and adapt statistical methods for analysis of multiple-electrode data from a series of experiments aimed at understanding the evolution of cortical activity in several areas of the brain while a monkey learns hand movement tasks. The results will lead to improvements in brain-controlled robotic devices for neural prostheses and, thus, will likely benefit people paralyzed by head or spinal cord trauma, amputees, and those with severe deficits caused by diseases such as stroke, amyotrophic lateral sclerosis, cerebral palsy, or multiple sclerosis.